Branch prediction techniques are frequently used in processors and other processing devices to enhance the performance of the processor. One type of branch prediction includes the speculative branch prediction whereby after a first conditional branch prediction is made, one or more speculative branch predictions may be made prior to resolution of the first conditional branch prediction, wherein the number of speculative branch predictions made is referred to the available speculative branch prediction depth. Speculative branch predictions often are advantageous in that if the conditional branch prediction preceding a speculative branch prediction is taken, the speculative branch prediction becomes a conditional branch prediction and instructions related to the new conditional branch prediction are already pre-fetched and available for execution by the pipeline of the processor. This results in a full utilization of the pipeline of the processor, thereby preventing stalls of the processor. However, speculative branch predictions may be disadvantageous in that if the first conditional branch prediction is not taken, the pipeline has wasted both time and power in pre-fetching and loading the instructions related with the speculative branch prediction into a cache and/or the pipeline itself. As a result, the processor also must expend energy and time forcing the pipeline and removing or ignoring data resulting from the execution of the erroneously predicted branch.
Accordingly, conventional processing devices may utilize a predetermined available speculative branch prediction depth in an attempt to achieve an optimal balance between the advantages and disadvantages of speculative branch prediction. However, as the speculative branch prediction hit/miss rate and the average number of conditional branches is highly application specific, the selection of any particular predefined available speculative branch prediction depth often proves sub-optimal for some or all of the applications executed by the processor. Accordingly, a system and method for adaptive speculative branch prediction depth optimization would be advantageous.